Senior AI Engineer - IV

Artech LLC

  • Miramar or Dallas, TX, FL
  • 3 days ago
  • $85–$90 Per Hour

Highlights

Operating as a senior independent contributor, this role works comfortably across multiple concurrent initiatives — spanning AI architecture, model integration, and developer tooling — while collaborating with Data Scientists, Data Engineers, product owners, and business stakeholders to translate complex requirements into production-ready AI solutions. Leverage AI coding assistants (Claude Code, GitHub Codex, and similar tools) to accelerate development, automate repetitive engineering tasks, and improve code quality across the team.

Numbers & Facts

LocationMiramar or Dallas, TX, FL
Salary$85–$90 Per Hour

Description

Job Title: Senior AI Engineer
Work Location: Miramar, FL or Dallas, TX (Remote but local candidates only)
Duration of Assignment: 12+ Months
Pay Rate Range: $85.00-$90.00/hr on W2
Description:
The Senior AI Engineer reports directly to the VP of AI and is responsible for leading and advancing enterprise-wide AI initiatives. This role leads the design, development, and deployment of end-to-end AI systems and intelligent agents that drive automation, decision-making, and business value at enterprise scale. Operating as a senior independent contributor, this role works comfortably across multiple concurrent initiatives — spanning AI architecture, model integration, and developer tooling — while collaborating with Data Scientists, Data Engineers, product owners, and business stakeholders to translate complex requirements into production-ready AI solutions. With deep expertise in enterprise AI architecture and hands-on proficiency with AI coding assistants such as Claude Code and Codex, this individual accelerates delivery velocity while maintaining rigorous engineering standards across the full AI development lifecycle.
Duties and Responsibilities
  • Design, build, and deploy end-to-end AI systems — from data ingestion and model development through inference, monitoring, and continuous improvement
  • Architect and develop AI agents and multi-agent frameworks capable of reasoning, planning, and executing complex workflows autonomously
  • Build cohesive AI solutions through the orchestration and integration of Models, LLMs, agentic services, expert systems, and knowledge graphs
  • Leverage AI coding assistants (Claude Code, GitHub Codex, and similar tools) to accelerate development, automate repetitive engineering tasks, and improve code quality across the team
  • Build and maintain scalable AI pipelines on Databricks and AWS, integrating with existing data infrastructure and enterprise systems
  • Define and implement enterprise AI architecture standards, patterns, and best practices across the organization
  • Evaluate and integrate large language models (LLMs), foundation models, and generative AI capabilities into business applications
  • Collaborate with Data Scientists to operationalize ML models and move experiments from prototype to production
  • Partner with cross-functional teams across multiple simultaneous initiatives to scope, design, and deliver AI-powered solutions
  • Establish model monitoring, evaluation, and feedback loops to ensure AI systems remain accurate, safe, and performant in production
  • Stay current with the rapidly evolving AI landscape and proactively recommend new tools, frameworks, and approaches that improve outcomes
  • Mentor junior engineers and contribute to a culture of technical excellence, experimentation, and continuous learning
  • Prepare technical documentation, architecture diagrams, and executive presentations to communicate AI strategy and results
Minimum Qualifications/Requirements
  • Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; master's degree preferred
  • 7+ years of experience in software or data engineering with at least 5 years focused on AI/ML systems development
  • Demonstrated end-to-end experience building and deploying AI systems and AI agents in production environments
  • Proficiency with AI coding assistants such as Claude Code, GitHub Codex, or equivalent tools as part of an active development workflow
  • Hands-on experience with Databricks for model training, feature engineering, and pipeline orchestration
  • Solid experience with AWS cloud services (SageMaker, Lambda, S3, EC2, Step Functions, or equivalent) for AI/ML workloads
  • Strong Python skills including SparkSQL, MLlib, PyTorch, spaCy, and NLTK for NLP and ML model development
  • Experience integrating AI systems via REST APIs, GraphQL, and OAuth for secure, scalable enterprise connectivity
  • Proven ability to operate as a senior independent contributor across multiple initiatives simultaneously without close supervision
  • Experience designing enterprise AI architecture including APIs, orchestration layers, vector databases, and model serving infrastructure
Preferred Skills
  • Experience building multi-agent systems and knowledge graphs using frameworks such as LangGraph, AutoGen, CrewAI, or the Anthropic Agent SDK
  • Familiarity with front-end and visualization technologies including React/Native, Figma, Dash or similar, and Bootstrap for building AI-powered user interfaces and data applications
  • Familiarity with prompt engineering, retrieval-augmented generation (RAG), and fine-tuning techniques for production LLM applications
  • Experience with MLOps practices including CI/CD for AI systems, model versioning, and automated evaluation pipelines
  • Knowledge of vector databases such as Pinecone, Weaviate, or pgvector for semantic search and retrieval applications
  • Familiarity with data governance, AI safety, and responsible AI principles in enterprise settings
  • Experience with Databricks Unity Catalog, Delta Lake, and MLflow for end-to-end model lifecycle management
  • Strong communication and stakeholder management skills — able to present technical AI concepts clearly to both engineering teams and business executives
  • Ability to evaluate build vs. buy tradeoffs for AI tooling and make architecture recommendations with long-term maintainability in mind
  • Experience contributing to AI strategy, roadmap planning, and organizational AI adoption initiatives
  • Attention to detail with a strong bias toward shipping reliable, well-documented, production-grade systems

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